Investigating Effectiveness of Cattle Farms

and Aerobic Digesters in the United States

by Production of Biogas Over Cattle
Headcount
Word count: 2217

Introduction: Identifying the Context

Greenhouse gases have become a defining point of climate change discussion. While there is a big
focus on anthropogenic sources, emissions influenced by people, many sources that exploit natural
processes are often left out of this conversation. As important perpetrators of the threat of global
warming, these sources need to be addressed.

Global warming stems from the natural process of the greenhouse effect (Cassia et al.). This is a
naturally occurring process where specific greenhouse gasses absorb and keep energy left by radiation
from the sun’s rays after it’s reflected off the surface of Earth. This conservation of energy keeps
temperatures and climates stable across the surface (Cassia et al.). Global warming happens when these
gasses accumulate and the energy lost is further reduced, resulting in destabilizing climate systems
(Woodwell and Mackenzie 17). A destructive feedback loop from melting ice caps and permafrost
areas, warming rising tides, and forest deaths (Cox et al., 2021). These disrupt natural systems that
regulate the greenhouse effect and result in compounding global warming (Dirmann, 2021). These
gasses include, by most to least occurring in the atmosphere, water vapor, carbon dioxide, methane,
and nitrous oxide (EPA, 2023).

Though global warming is heavily attributed to carbon dioxide and heavy manufacturing, there is a
less-discussed source. The impacts of methane, particularly in natural processes from agricultural
practices, make up a lot of the demographics in methane production, a gas making up 16% of the total
greenhouse effect (EPA, 2023). For example, cow metabolism as a natural process, uses enteric
fermentation to digest food. This kind of process exudes methane, which in environments like farms, is
released into the atmosphere in mass numbers by giant herds (Jones et al., 2021).

Specifically with this process, new advances in cow technology propose a solution to this problem.
Better agricultural infrastructure and efforts towards sustainability have been shown to lower methane
footprints. A great example of this is anaerobic digesters. These are systems used in cattle farms to
harness excessive gases from digestion and pump out cleaner renewable energy. These systems take the
methane released from digestion and use its released energy through combustion to power electrical
grids, offsetting methane emissions and replacing fossil-fueled energy (Clemens et al., 2021). Since a
Cow can produce from 250 to 500 liters of methane gas per day (Johnson & Johnson, 1995), the 87.2
million cows (USDA, 2024) in US farms could use digestors to supply cities with cleaner renewable
energy while not only offsetting but controlling this source's greenhouse effects. There are, however,
many problems and inefficiencies in these digesters. These systems are still relatively new and come
with their own sets of problems, most relevantly, size. Their implementation on different herd sizes
leads to widely varied outputs (EPA, 2023), leading me to question if the efficiency of these systems is
compatible with our current systems of cattle production. More specifically, if the challenges related to
herd sizes have a significant change in the ending result, the total methane captured.
Research Question:

Is there a significant impact between the rate of methane gas being caught by
anaerobic digestors and herd sizes of cattle livestock in US farms?

My research question is based on the rates of greenhouse gas emissions in

cattle livestock. More specifically, I’m narrowing down my data to cattle in
the United States. By finding the differences in rates between different heard
sizes, I can estimate if the methane is being recycled effectively at higher
scales. A significant correlation between Herd sizes and Methane can support
the conclusion that there's a trend line I can use to infer efficiency.

Planning

Method:
1) variable; starting with 0-2000 cows in a herd and going up by 2000 until I get to 10000, (0-2000,
2000-4000, 4000-6000, 6000-8000, 8000-10000)
2) I recorded data on yearly methane containment and headcount of cattle from randomly chosen
farms with Aerobic Digesters. By using the random number generator provided by Google.com,
I filled my five levels each with 5 data points from the “Livestock Anaerobic Digester Database”
from the U.S. Environmental Protection Agency.
3) With this data, calculated the average volume of methane captured for every level of herd size
and recorded it on another column of the Excel document
4) I put the data from the table into a bar graph (Figure a) and added the standard deviation lines by
using the mean of each level and the data points of each level with the Excel function, =STDEV()
5) With my information, I Found Pearson’s Correlation Coefficient to determine either positive,
negative or no correlation between my data points.

Sampling Strategy:

Since I couldn’t take readings from farms myself, I relied on the statistics
from a government agency. The Anaerobic Digester Database by the
Environmental Protection Agency recorded the products produced by the
digester as biogas. Since the makeup of biogas can be between 50-70
percent methane(Tanigawa, 2017), I’m using biogas as my measure to
reflect methane since the conversion could be skewed between farms.
Furthermore, I used the random number generator from Google to choose
the farms with recorded digestor information. I split them up into 5 groups
so I could data from multiple groups and get more accurate averages. This
way the data I use can better reflect the skew of the rest of the data from the
database. The standard deviation of each group also helped demonstrate the
variability of data while Pearson's Correlation coefficient can describe how
well the variability still followed a trend.

Risk Assessment:

I only used data from refutable and public databases so there was no
 actual testing of any animals or danger involved with my collection
process. The data points I use only list data from digestor projects and
don’t include individual descriptors to prevent any impact on any
person's reputation.

Ethical considerations:

I shouldn't blame or critique the farmers or agencies behind the project's

faults. These digesters are still new tech and in different levels of
development. Farms aren’t required to make this change either. Additionally,
I’m respecting farmer's privacy and consent by only accessing their farm
data from a public source.

Results and Analysis

Data Presentation:
Figure 1 - Change in biogas production in anaerobic digestors with varying herd sizes:

Change in biogas production in anaerobic digesters with varying herd sizes

headcount of
cattle Biogas Production feet cubed per day Average
0-1999 170,206 78,765 60,561 432,000 116,328 171,572
2000-3999 329,000 165,000 223,000 429,000 165,000 262,200
4000-5999 387,360 634,800 387,360 235,000 250,000 378,904
6000-7999 360,000 325,000 340,000 360,000 340,000 345,000
8000-9999 459,250 459,250 415,000 576,000 270,000 435,900
Calculated R-value = 0.6349960191
United States Environmental Protection Agency. (2024).. Retrieved 1 April 2024, from
https://www.epa.gov/agstar/livestock-anaerobic-digester-database
Figure 2 - Change in biogas production in anaerobic digestors with varying herd sizes:

Figure 3 - Raw Data of chosen point with Herd size and biogas produced with trend line.
Interpretation of Data:

The data shows a trend of rising biogas production as the cattle herd sizes rise too. This is
supported by Pearson’s correlation coefficient of about 0.634 from Figure 1. The
moderate positive correlation signifies a moderate trend to the upper right of the graph.
Specifically, the data also shows this growth of Cattle and biogas produced to be
disproportionately rising. It could be assumed that as the cattle sizes double and increase,
so should the biogas double in production. Figure 2 of my data, however, shows that
averages in biogas production after the 4000-6000 herd mark, stop following that trend.
Here groups 0-1999(red) group averages jump by around 116,704ft^3 of biogas to 2000-
3999(blue), but there's also a shift of 90,900ft^3 before group 8000-9999(yellow), and
after going down -33,904ft^3 to the previous group, 6,000-8000(purple). These
differences support the conclusion that the production of biogas from anaerobic Cattle
digesters becomes less efficient due to rising herd numbers. This could be explained by
digesters in cattle farms to have a certain limit. These systems, since relatively new, all
have different levels of implementation. Cattle farms with larger herds could find it
harder to implement digestors as effectively as they could on a smaller scale. This
assertion can be supported by the varying levels of standard deviation in Figure 2. The
data points had relatively high standard deviations from each other. This could suggest
each farm had different levels of implementation that further affected the production of
biogas over bigger herds. This can be challenged by the range of standard deviations of
the 6000-7000 cattle row. There were many outliers and variability in my groups, as seen
in Figure 3, except for the 4th column. Whereas the points lined up with only a standard
deviation of 15,000, the rest of the points had deviations of higher than 100,000. This
change can suggest that the levels of implementation aren’t factors as these points were
close to equal with little variation from any other observable factor. However, the lack of
outliers here can also be interpreted as an outlier in the data itself. The source for my data
had many more points at the 6000-7000 mark than any other group. These points were
also all congruent and followed the same names and locations. This could mean that this
part of my graph is the least affected by herd size but by the congruent level of
implementation of these points. In conclusion, the reasons I listed support my conclusion
that there is a general diminishing return in biogas produced as herd sizes grow, with a lot
of standard deviation and outliers between points.

Discussion and Evaluation

Discussion:

Following my Data, anaerobic digestors are less effective at large scale. This conclusion
could suggest that these methane up-cyclers may serve a better purpose as renewable
energy sources than any efficient method to control greenhouse gas emissions currently.
Though greenhouse gases have gone down significantly from 1990 to 2021, this
decrease came from the control of CO2. Methane, on the other hand, has been on the
rise, with new studies showing shifting dietary practices focusing heavily on dairy. With
this in mind, digesters of greenhouse gasses can be considered ill-equipped with the way
the cattle production system is set up, and suggest that a larger change in this system is
needed to keep herds smaller and manageable..

Strengths, Weaknesses and Limitations:

There are many strong points in my approach. Firstly, the secondary data was
measured by a legitimate agency with precise accuracy making the data reliable.
Though there were a lot of outliers in my data, I think its inclusion reflects the
variability of the data well. There is a chance though, that this inclusion skewed my
data too far to show an accurate trend in the data. Since my data was compiled by all
these farms separately, the amount of contributors to this database gives my data some
legitimacy. The tech is new and hasn't been given levels of implementation so many of
these points could be hybrids of different models with different inputs and outputs,
leaving many points out of my research. I also can’t find much on how the biogas is
caught and measured by each farm, a possibility that many of the contributors went by
measuring in different ways.

Modifying the Method:

To improve my method I would first start by counting out all the data points and converting the ones that were listed
with different measurements of the digestor information to have more data to pull from. I would also use way more data
points so extreme outliers would less affect my data. I would also introduce more groups of cattle headcounts so I could
better see the trendline between averages. Most importantly I can record how different levels of implementation can
skew my data to better focus on one variable.

Further Areas of Research:

To go further in-depth into the release of greenhouse gasses and solutions for climate
change, I could find the expected amounts of methane per cow at each point and take
away the biogas produced. This number I could convert to find the carbon offset of the
energy produced and if that upcycling is what better addresses the threats of climate
change.

Applications

The projects from the database are good information on what works best in catching high
amounts of methane. By keeping herds smaller, according to the data, there would be
more methane caught and biogas produced. Bigger herds could have problems in the
implementation of these digesters. Keeping them separate allows for better data to be
collected since there will be less variables possibly skewing the data. The data can also
be applied to cows feeding. Since many different foods give off different byproducts
during metabolism, we could find a better source of food for cows that allows for less
production of methane. By making sure the process is as efficient as possible, the energy
being created can offset the carbon footprint of meat and other agricultural practices.
These solutions though, rely on the development of this kind of tech as well as the easy
implementation and cost-effective way to get these systems to cow farms globally
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